Predictive performance models are important tools that support system sizing, capacity planning, and systems management exercises. We introduce the Weighted Average Method (WAM) to improve the accuracy of analytic predictive performance models for systems with bursts of concurrent customers. WAM considers the customer population distribution at a system to reflect the impact of bursts. The WAM approach is robust with respect to distribution functions, including heavy-tail-like distributions, for workload parameters. We demonstrate the effectiveness of WAM using a case study involving a multitier TPC-W benchmark system. To demonstrate the utility of WAM with multiple performance modeling approaches, we developed both Queuing Network Models and Layered Queuing Models for the system. Results indicate that WAM improves prediction accuracy for bursty workloads for QNMs and LQMs by 10 and 12 percent, respectively, with respect to a Markov Chain approach reported in the literature.